Diffractive coupling for photonic networks: how big can we go?

Abstract

Photonic networks are considered a promising substrate for high-performance future computing systems. Compared to electronics, photonics has significant advantages for a fully parallel implementation of networks. A promising approach for parallel large-scale photonic networks is realizing the connections using diffraction. Here, we characterize the scalability of such diffractive coupling in great detail. Based on experiments, analytically obtained bounds and numerical simulations considering real-world optical imaging setups, we find that the concept in principle enables networks hosting over a million optical emitters. This would be a breakthrough in multiple areas, illustrating a clear path toward future, large scale photonic networks.

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• All-Optical Machine Learning Using Diffractive Deep Neural Networks
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Cited by in corpus (9)

• Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit
• Large-Scale Optical Reservoir Computing for Spatiotemporal Chaotic Systems Prediction
• Developing of a photonic hardware platform for brain-inspired computing based on $5\times 5$ VCSEL arrays
• Experimental reservoir computing with diffractively coupled VCSELs
• Boolean learning under noise-perturbations in hardware neural networks
• Optical pumping of quantum dot micropillar lasers
• Injection locking and coupling the emitters of large VCSEL arrays via diffraction in an external cavity
• Advancing Fourier: space-time concepts in ultrafast optics, imaging and photonic neural networks
• A spiking photonic neural network of 40.000 neurons, trained with rank-order coding for leveraging sparsity